[1]王正.有导师学习神经网络的大米识别[J].机械与电子,2018,(05):67-70.
　WANG Zheng,Classification and Recognition of Rice Based on Supervised Learning Neural Network[J].Machinery & Electronics,2018,(05):67-70.
点击复制

有导师学习神经网络的大米识别
分享到：

《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2018年05期
页码:
67-70
栏目:
智能工程
出版日期:
2018-05-24

文章信息/Info

Title:
Classification and Recognition of Rice Based on Supervised Learning Neural Network
文章编号:
1001-2257(2018)05-0067-04
作者:
王正1;  2
(1.宁夏大学新华学院,宁夏 银川 750021; 2.宁夏大学土木与水利工程学院,宁夏 银川 750021)
Author(s):
WANG Zheng1;  2
(1. College of Xinhua, Ningxia University, Yinchuan 750021,China; 2.School of Civil Engineering and Hydraulic Engineering, Ningxia University, Yinchuan 750021,China)
关键词:
神经网络;  神经元阈值;  权值矩阵;  高斯函数
Keywords:
neural network;  neuron threshold;  weight matrix;  gauss function
分类号:
TP183
文献标志码:
A
摘要:
为了利用智能算法来识别大米的种类属性,提出一种基于有导师学习神经网络的大米识别方法。该方法将训练样本的参数数据构造成测试集的标准数据,建立每种稻米的GRNN和PNN模型,并利用MATLAB软件对包含有不同几何特征参数的GRNN和PNN模型进行仿真。在已有训练样本数据的指导下,分析稻米米粒几何参数与稻米种类之间的关系,识别出其他样本的类别。结果显示,提出的方法能够精准地识别稻米的种属,而且GRNN模型的准确率要低于PNN模型,该方法容易实现,无庞杂计算,避免繁琐迭代过程。
Abstract:
The paper main focus on intelligent algorithms for rice classification, and establish a new method based on supervised learning neural network. The parameter of training sample is considered as standard data for testing sample, the model of GRNN and PNN are carried out respectively to each rice and MATLAB is employed to simulate the previous models with different geometric characteristic, and moreover, the relationship between rice geometric parameters and rice varieties is analyzed. The results evidently pointed out to identify accurately and a good effect with the method, and GRNN's accuracy rate is a little lower than PNN's. In addition, the vital advantage to this approach is not only the simplicity to implement without iteration, but also a small amount of calculation to employ.

参考文献/References:

[1]胡桂仙,王俊,王建军,等.基于电子鼻技术的稻米气味检测与品种识别[J].浙江大学学报(农业与生命科学版),2011,37(6):670-676.
[2]史忠植.神经网络[M].北京:高等教育出版社,2009.
[3]张良均,曹晶,蒋世忠.神经网络实现教程[M].北京:机械工业出版社,2008.
[4]殷莹.基于广义回归神经网络无参考模糊图像质量评价[J].激光与红外,2013,43(4):466-70.
[5]LI C F,BOVIK A C,WU X J.Blind image quality assessment using a general regression neural network[J].IEEE Transaction on Neural Network,2011,22(5):793-799.
[6]元朴康,况盛坤,王强,等.基于GRNN的模糊图像盲评价[J].包装工程,2016,37(13):195-200.
[7]刘辉,张云生,张印辉,等.基于火焰图像特征与GRNN的转炉吹炼状态识别[J].计算机工程与应用,2011,47(26):7-10.
[8]丁硕,常晓恒,巫庆辉,等.基于GRNN与BPNN的二维向量模式分类对比研究[J].国外电子测量技术,2014,33(5):56-58.
[9]孙俊,路心资,张晓东,等.基于高光谱图像的红豆品种GA-PNN神经网络鉴别[J].农业机械学报,2016,47(6):215-221.
[10]李伦波,马广富.基于PNN的退化交通标志图像的识别算法研究[J].电子与信息学报,2008,30(7):1703-1707.
[11]闫凤,王立中,石磊,等.基于DWT和PNN的数字图像水印算法[J].湘潭大学自然科学学报,2016,38(3):89-93.
[12]李汶虹,王建国.结合Bi-2DPCA和PNN集成的SAR图像目标识别[J].中国电子科学研究院学报,2014,9(4):401-407.
[13]谢聪.基于CGA-PNN的高速公路交通事件检测算法研究[D].成都:西南交通大学,2016.
[14]高全华,王晋国,孙锋利.基于Pseudo-Zernike不变矩的PNN车牌汉字识别[J].计算机工程,2009,35(4):196-198.
[15]杨露,沈怀荣.基于HHT/PNN的故障信息融合诊断方法[J].北京工业大学学报,2010,36(2):152-157.
[16]申戬林,王灵梅,孟恩隆.改进小波包联合PNN的风电故障诊断研究[J].可再生能源,2014,32(4):412-17.
[17]杨蜀秦.农作物籽粒的图像处理和识别方法研究[D].咸阳:西北农林科技大学,2012.
[18]段海波,马晓年,李超,等.卡尔费休法在大米水分测定中的应用[J].安徽农业科学,2014,42(10):3056-3057.

相似文献/References:

[1]贾勇,马戎,李岁劳.MEMS陀螺零偏温度补偿[J].机械与电子,2019,(03):18.
　JIA Yong,MA Rong,LI Suilao. Bias Temperature Compensation Method of MEMS Gyroscope [J].Machinery & Electronics,2019,(05):18.
[2]李 力,陆金桂.基于PSO-BP神经网络的飞灰含碳量测量方法[J].机械与电子,2019,(04):68.
　.Prediction Method of Carbon Content in Fiy Ash Based on PSO-BP Neural Network[J].Machinery & Electronics,2019,(05):68.
[3]方 明,王 春,李松有.基于 ＤＡ- ＳＶＲ的２Ｍ 继电保护通道在线监测和故障诊断[J].机械与电子,2020,(02):63.
　,On-line Monitoring and Fault Diagnosis of 2M Relay Protection Channel Based on DA-SVR[J].Machinery & Electronics,2020,(05):63.
[4]张晓军.基于神经网络模糊控制的单目无人机视觉避障方法研究[J].机械与电子,2020,(06):41.
　ZHANG Xiaojun.Research on Visual Obstacle Avoidance Method of Monocular UAV Based on Neural Network Fuzzy Control[J].Machinery & Electronics,2020,(05):41.
[5]陈 壮,王宪伦,陈 闪.基于优化ADRC的单臂机器人轨迹跟踪研究[J].机械与电子,2020,(10):77.
　CHEN Zhuang,WANG Xianlun,CHEN Shan.Study on Trajectory Tracking of Single-arm Robot Based on Optimized ADRC[J].Machinery & Electronics,2020,(05):77.
[6]吕栋腾 １,雷涛峰 ２.基于神经网络的火电厂脱硫控制系统研究[J].机械与电子,2021,(09):37.
　LU Dongteng,LEI Taofeng.Research on Desulfurization Control System of Thermal Power Plant Based on Neural Network[J].Machinery & Electronics,2021,(05):37.
[7]平华丽.块状食品枕式包装机多轴同步控制[J].机械与电子,2022,(01):68.
　PING Huali.Multi Axis Synchronous Control of Pillow Packing Machine for Block Food[J].Machinery & Electronics,2022,(05):68.
[8]冯 斌.基于神经网络的汽车发动机机械设备故障定位[J].机械与电子,2022,(12):39.
　FENG Bin.Fault Location of Automobile Engine Mechanical Equipment Based on Neural Network[J].Machinery & Electronics,2022,(05):39.
[9]刘 方,李 慧,张林岗,等.基于神经网络的卷烟共线分拣系统自适应控制[J].机械与电子,2023,41(01):59.
　LIU Fang,LI Hui,ZHANG Lingang,et al.Adaptive Control of Cigarette Collinear Sorting System Based on Neural Network[J].Machinery & Electronics,2023,41(05):59.
[10]谢崇权,赵一夫,王安宽.基于神经网络的卷烟包装机运行监测与故障分析研究[J].机械与电子,2023,41(03):23.
　XIE Chongquan,ZHAO Yifu,WANG Ankuan.Research on Operation Monitoring and Fault Analysis of Cigarette Packing Machine Based on Neural Network[J].Machinery & Electronics,2023,41(05):23.

备注/Memo

备注/Memo:
收稿日期：2017-10-18
基金项目：宁夏高等学校科学研究项目（NGY2016215）
作者简介：王正（1988-），男，宁夏银川人，助教，研究方向为智能算法和智能控制。
更新日期/Last Update: 2018-05-24